126 citations · 209 across the 9 of their papers we have counts for
4 papers · 1 filter
Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?
Hang Jung Ling, Nathan Painchaud, Pierre-Yves Courand +3
Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by e…
Echocardiography Segmentation with Enforced Temporal Consistency
Nathan Painchaud, Nicolas Duchateau, Olivier Bernard +1
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer…
On the effectiveness of GAN generated cardiac MRIs for segmentation
Youssef Skandarani, Nathan Painchaud, Pierre-Marc Jodoin +1
In this work, we propose a Variational Autoencoder (VAE) - Generative Adversarial Networks (GAN) model that can produce highly realistic MRI together with its pixel accurate ground…
Cardiac MRI Segmentation with Strong Anatomical Guarantees
Nathan Painchaud, Youssef Skandarani, Thierry Judge +3
Recent publications have shown that the segmentation accuracy of modern-day convolutional neural networks (CNN) applied on cardiac MRI can reach the inter-expert variability, a gre…